Improving Deep Learning Based Anomaly Detection on Multivariate Time Series Through Separated Anomaly Scoring. - Adam Lundström; Mattias O'Nils; Faisal Z. Qureshi; Axel Jantsch (IEEE Access, 2022)

An Interactive Threshold-Setting Procedure for Improved Multivariate Anomaly Detection in Time Series. - Adam Lundström; Mattias O'Nils; Faisal Z. Qureshi (IEEE Access, 2023)

Factory-Based Vibration Data for Bearing-Fault Detection. - Adam Lundström; Mattias O'Nils (Data, 2023)

Towards Practically Applicable Transfer Learning Methods for Remaining Useful Life Prediction of Bearings. - Adam Lundström; Mattias O'Nils; Faisal Z. Qureshi (INDIN, 2024)

Contextual Knowledge-Informed Deep Domain Generalization for Bearing Fault Diagnosis. - Adam Lundström; Mattias O'Nils; Faisal Z. Qureshi (IEEE Access, 2024)

Barriers to Adopting Design for Assembly in Modular Product Architecture: Development of a Conceptual Model Through Content Analysis. - Fabio Marco Monetti; Adam Lundström; Antonio Maffei (CoRR, 2024)